Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images. It is generally treated as a retrieval problem, where the affinities between the probe image and gallery images (P2G affinities) are used to rank the retrieved gallery images. However, most existing methods only consider P2G affinities but ignore the affinities between all the gallery images (G2G affinity). Some frameworks incorporated G2G affinities into the testing process, which is not end-to-end trainable for deep neural networks. In this paper, we propose a novel group-shuffling random walk network for fully utilizing the affinity information between gallery images in both the training and testing processes. The proposed approach aims at end-to-end refining the P2G affinities based on G2G affinity information with a simple yet effective matrix operation, which can be integrated into deep neural networks. Feature grouping and group shuffle are also proposed to apply rich supervisions for learning better person features. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by large margins, which demonstrate the effectiveness of our approach.

本文提出了一种基于群组洗牌随机游走网络的新方法，以充分利用训练和测试过程中图库图像之间的相关信息。该方法旨在通过简单而有效的矩阵操作，在基于图库图像之间的相关性信息的基础上完全细化P2G相似度，并可整合到深度神经网络中。在Market-1501、CUHK03和DukeMTMC数据集上，该方法优于现有技术，证明了该方法的有效性。

深度群随机行走用于人员再识别